Using R in hydrology (EGU2017 short course)

Instructors: Shaun Harrigan, Katie Smith, Berry Boessenkool and Daniel Klotz
Organizer: Berry Boessenkool, PhD student at Potsdam University (Germany)
Contact: Questions and feedback are welcome via berry-b@gmx.de

These slides and all other course materials can be found at
github.com/brry/rhydro
Download the github course repository with all the materials including the datasets and presentation source code.
This is an R Markdown Notebook.
For discussions, please visit the Hydrology in R Facebook group.
Before running the code blocks below, we suggest to get package installation instructions by running:

source("https://raw.githubusercontent.com/brry/rhydro/master/checkpc.R")


Aim and contents of this workshop

We want to:

  • Show off how awesome R is for hydrology (it’s R-some!^^)
  • Convince you to start or continue using R
  • Provide all the code for you as a starting point

We can not:

  • Teach you actual R coding (90 mins is too short for a tutorial)

We have prepared:

 

Before we get started, please let us know your current R knowledge level by filling out the short survey at
bit.ly/knowR


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Report

Good coding practice, report generation (Rstudio, rmarkdown, R notebook)
Daniel Klotz

Introduction

goals

goals

Why I use R

Why I did not use:

equals

equals

Whats great about R:

  library(ggplot2)
  test_data <- mpg
  test_plot <- ggplot(test_data, aes(displ, hwy, colour = class)) + 
    geom_point()
  test_plot

Why I decided to use R:

pipe

pipe

Previously:

  aggregation_function <- function(x) {
    round(mean(x),2)
  }
  mtcars_subset <- subset(mtcars,hp > 100)
  mtcars_aggregated <- aggregate(. ~ cyl, data = mtcars_subset, FUN = aggregation_function)
  car_data1 <- transform(mtcars_aggregated, kpl = mpg*0.4251)
  print(car_data1)

Now:

library(magrittr)
car_data2 <- 
  mtcars %>%
  subset(hp > 100) %>%
  aggregate(. ~ cyl, data = ., FUN = . %>% mean %>% round(2)) %>%
  transform(kpl = mpg %>% multiply_by(0.4251)) %>%
  print

btw: You can integrate other programming languages with ease. Here an example from Yihui Xie for the use of Fortran in Rmarkdown:

  1. Compile Code:

    C Fortran test
      subroutine fexp(n, x)
      double precision x
C  output
      integer n, i
C  input value
      do 10 i=1,n
         x=dexp(dcos(dsin(dble(float(i)))))
  10  continue
      return
      end

  2. Run Code:

    res = .Fortran("fexp", n=100000L, x=0)
str(res)

Be happy with the result: > ## List of 2 > ## $ n: int 100000 > ## $ x: num 2.72

Markdown

HTML: HyperText Markdown Language

John Gruber:

John Gruber

Comparison: Markdown vs. Latex Comparison

Rstudio provides cheat-sheets with the most important informations about many of their “favorite” packages & software:

Cheat Sheet

Rmarkdown

In Rstudio:

Rmark1

Rmark2

Rmark2

“Native” Formats:

Much more possible if you adress pandoc directly: pandoc

Information in the text can be automatically updated with the rest of the document: [time for coffee

Examples

Small Websites

Books (blogdown)

bookdown1 bookdown2

Blogs (hugo)

hugo

Presentations

bookdown1

Widgets

cran-gauge superzip

Apps (Shiny)

shiny shiny2


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GIS

Using R as GIS (reading a rainfall shapefile + Kriging, sf + leaflet + mapview + OSMscale)
Berry Boessenkool

Shapefiles

Reading shapefiles with maptools::readShapeSpatial and rgdal::readOGR is obsolete.
Instead, use sf::st_read. sf is on CRAN since oct 2016.
Main reaction when using sf: “Wow, that is fast!”
Download the shapefile or better: download the whole github course repository

rain <- sf::st_read("data/PrecBrandenburg/niederschlag.shp")

Central points of rainfall Thiessen polygons

centroids <- sf::st_centroid(rain)
centroids <- sf::st_coordinates(centroids)
centroids <- as.data.frame(centroids)

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Plotting, maps

Static plot:

plot(rain[,1])

Static map:

prj <- sf::st_crs(rain)$proj4string
cent_ll <- OSMscale::projectPoints(Y,X, data=centroids, to=OSMscale::pll(), from=prj)
#map_static <- OSMscale::pointsMap(y,x, cent_ll, fx=0.08, type="maptoolkit-topo", zoom=6)
#save(map_static, file="data/map_static.Rdata")
load("data/map_static.Rdata")
OSMscale::pointsMap(y,x, cent_ll, map=map_static)

Interactive map:

library(leaflet)
cent_ll$info <- paste0(sample(letters,nrow(cent_ll),TRUE), ", ", round(cent_ll$x,2), 
                       ", ", round(cent_ll$y,2))
leaflet(cent_ll) %>% addTiles() %>% addCircleMarkers(lng=~x, lat=~y, popup=~info)

Interactive map of shapefile:

# make sure to have installed the development version of mapview: 
# devtools::install_github("environmentalinformatics-marburg/mapview", ref = "develop")
library(berryFunctions) # classify, seqPal
col <- seqPal(n=100, colors=c("red","yellow","blue"))[classify(rain$P1)$index]
mapview::mapview(rain, col.regions=col)

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Kriging

Plot original points colored by third dimension:

pcol <- colorRampPalette(c("red","yellow","blue"))(50)
x <- centroids$X # use cent_ll$x for projected data
y <- centroids$Y
berryFunctions::colPoints(x, y, rain$P1, add=FALSE, col=pcol)

Calculate the variogram and fit a semivariance curve

library(geoR)
geoprec <- as.geodata(cbind(x,y,rain$P1))
vario <- variog(geoprec, max.dist=130000) # other maxdist for lat-lon data
fit <- variofit(vario)
plot(vario)
lines(fit)

Determine a useful resolution (keep in mind that computing time rises exponentially with grid size)

# distance to closest other point:
d <- sapply(1:length(x), function(i)
            min(berryFunctions::distance(x[i], y[i], x[-i], y[-i])) )
# for lat-long data use (2017-Apr only available in github version of OSMscale)
# d <- OSMscale::maxEarthDist(y,x, data=cent_ll, fun=min)
hist(d/1000, breaks=20, main="distance to closest gauge [km]")
mean(d/1000) # 8 km

Perform kriging on a grid with that resolution

res <- 1000 # 1 km, since stations are 8 km apart on average
grid <- expand.grid(seq(min(x),max(x),res),
                    seq(min(y),max(y),res))
krico <- krige.control(type.krige="OK", obj.model=fit)
#krobj <- krige.conv(geoprec, loc=grid, krige=krico)
#save(krobj, file="data/krobj.Rdata")
load("data/krobj.Rdata") # line above is too slow for recreation each time

Plot the interpolated values with image or an equivalent function (see Rclick 4.15) and add contour lines.

par(mar=c(0,3,0,3))
geoR:::image.kriging(krobj, col=pcol)
colPoints(x, y, rain$P1, col=pcol, legargs=list(horiz=F, title="Prec",y1=0.1,x1=0.9))
points(x,y)
plot(rain, col=NA, add=TRUE)


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Discharge

River discharge time-series visualisation and extreme value statistics (animation + extremeStat)
Berry Boessenkool

Read, plot and aggregate data

Datasets from http://nrfa.ceh.ac.uk/data/station/meanflow/39072
Download discharge1, discharge2_xxx, discharge3_xxx, or better: download the whole github course repository

Read and transform data

Q <- read.table("data/discharge39072.csv", skip=19, header=TRUE, sep=",", fill=TRUE)[,1:2]
colnames(Q) <- c("date","Royal_Windsor_Park")
Q$date <- as.Date(Q$date, format="%Y-%m-%d")

Examine data

head(Q)
str(Q)

Simple time series plot

plot(Q, type="l", col="blue")

Publication-ready graphics

png("DischargeVis.png", width=20, height=10, units="cm", res=500)
#pdf("DischargeVis.pdf", width=20/2.5, height=10/2.5) # vector graph
par(mar=c(3.5,3.5,2.5,0.2), mgp=c(2.3,0.7,0), las=1)
plot(Q, type="l", col="blue", main="NRFA: Thames\nRoyal Windsor Park",
     xlab="Date", ylab="Discharge  [m\U{00B3}/s]")
dev.off()

Annual maxima, German hydrological year split at Oct 31

Q$hydyear <- as.numeric(format(Q$date+61, "%Y"))
annmax <- tapply(Q$Royal_Windsor_Park, Q$hydyear, max, na.rm=TRUE)
annmax <- annmax[-1]
hydyear <- as.numeric(names(annmax)) 
plot(hydyear, annmax, type="o", las=1)

Extreme value statistics

library(extremeStat)
dlf <- distLfit(annmax)
plotLfit(dlf)
plotLfit(dlf, cdf=TRUE)
dle <- distLextreme(dlf=dlf, RPs=c(5,10,50,100), gpd=FALSE)
plotLextreme(dle)

Logarithmic plot with many fitted distribution functions

plotLextreme(dle, nbest=16, log=TRUE)

Return values (discharge estimates for given return periods)

dlf$returnlev

In reality, please use non-stationary EVS!

Uncertainty band for Wakeby distribution fit estimate

dle_boot <- distLexBoot(dle, n=10, nbest=1)
plotLexBoot(dle_boot, distcol="green")

More details in the package vignette

vignette("extremeStat")

Animated movie

Read data from several discharge stations

if(FALSE){
Q2 <- read.table("data/discharge_____.csv", skip=19, header=TRUE, sep=",", fill=TRUE)[,1:2]
colnames(Q2) <- c("date","dummy1");  Q2$date <- as.Date(Q2$date, format="%Y-%m-%d")
Q3 <- read.table("data/discharge_____.csv", skip=19, header=TRUE, sep=",", fill=TRUE)[,1:2]
colnames(Q3) <- c("date","dummy2");  Q3$date <- as.Date(Q3$date, format="%Y-%m-%d")
}
Q2 <- Q ; Q2[,2] <- Q2[,2] + 100 ; Q2 <- Q2[-(1:100),]
Q3 <- Q ; Q3[,2] <- Q3[,2] -  50 ; Q3 <- head(Q3, -365*3)
colnames(Q2)[2] <- "dummy1"; colnames(Q3)[2] <- "dummy2"

Merge datasets

dis <- Reduce(function(...) merge(..., all=T), list(Q,Q2,Q3))

Code to generate one movie slice

library(berryFunctions) # for lim0, monthAxis, textField, etc

scene <- function(i, vlcnote=TRUE, cex=1.2)
{
 sel <- 0:120
 dis2 <- dis[i + sel, ]
 stat <- c("Royal_Windsor_Park", "dummy1", "dummy2")
 col <- c("red", "blue", "orange")
 names(col) <- stat
 plot(dis2$date,dis2$Royal_Windsor_Park, type="n", ylim=lim0(500),  las=1, 
      xaxt="n", yaxt="n", cex.lab=cex, xlab="", 
      ylab="Discharge  [m\U{00B3}/s]", xaxs="i")
 axis(2, cex.axis=cex, las=1)
 Sys.setlocale("LC_TIME", "C")
 monthAxis(midmonth=TRUE, format="%b\n%Y", cex=cex, mgp=c(3,3.5,0))
 abline(h=1:6*100, v=dis2$date[format(dis2$date,"%d")=="01"], col=8)
 for(s in stat) lines(dis2$date, dis2[,s], lwd=4, col=col[s])
 xi <- seqR(sel,len=length(stat)+2)[-1]
 xi <- head(xi, -1)
 textField(dis2$date[xi], diag(as.matrix(dis2[xi,stat])), stat, cex=cex, col=col)
 box()
 if(vlcnote) mtext("VLC: 'e': single frame forward\n'SHIFT+LEFT': few seconds back",
                   side=3, line=-9, outer=TRUE, adj=0.95, cex=cex)
}

par(mar=c(5,5,0.5,0.5), mgp=c(3,0.7,0))
scene(150)

Actual movie

library(animation)
saveVideo({par(mar=c(6,8,1,1), mgp=c(5.5,0.7,0))
 dummy <- pbsapply(seq(0, by=3, len=50), scene, cex=2); rm(dummy)
}, video.name="Qmovie.mp4", interval=0.1, ffmpeg="/usr/bin/ffmpeg", 
ani.width=7*200, ani.height=5*200)

Open video in default mp4player


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Hydmod

Hydrological modelling with airGR
Katie Smith


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EDA

Exploratory Data Analysis including flow duration curve and trend analysis on time-series
Shaun Harrigan


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Discussion

Please give us feedback at bit.ly/feedbackR

For discussions, please use the Hydrology in R Facebook group.